---
title: "Who cites The NarrativeQA Reading Comprehension Challenge…"
description: "The papers of the Elamur Research library that cite «The NarrativeQA Reading Comprehension Challenge», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The NarrativeQA Reading Comprehension Challenge](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/) / cited by

# Who cites «The NarrativeQA Reading Comprehension Challenge» · page 3

435 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/2/) · **3** · [4](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/5/)

1. [Rationale-based Learning Using Self-Supervised Narrative Events for Text Summarisation of Interactive Digital Narratives](https://aclanthology.org/2024.lrec-main.1186/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
2. [Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach](https://aclanthology.org/2024.emnlp-industry.66/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track*
3. [SEGMENT+: Long Text Processing with Short-Context Language Models](https://aclanthology.org/2024.emnlp-main.926/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
4. [Searching for Best Practices in Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.981/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
5. [Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue](https://aclanthology.org/2024.inlg-main.15/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference*
6. [Stable LM 2 1.6B Technical Report](https://arxiv.org/abs/2402.17834) *— 2024 · arXiv:2402.17834*
7. [SyllabusQA: A Course Logistics Question Answering Dataset](https://aclanthology.org/2024.acl-long.557/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya](https://aclanthology.org/2024.lrec-main.1404/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
9. [TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding](https://arxiv.org/abs/2404.11912) *— 2024 · arXiv:2404.11912*
10. [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://aclanthology.org/2024.lrec-main.1481/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
11. [tinyBenchmarks: evaluating LLMs with fewer examples](https://arxiv.org/abs/2402.14992) *— 2024 · arXiv:2402.14992*
12. [大语言模型故事理解能力评价数据集(Benchmarking story comprehension ability of large language model)](https://aclanthology.org/2024.ccl-1.60/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
13. [A Length-Extrapolatable Transformer](https://aclanthology.org/2023.acl-long.816/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information](https://aclanthology.org/2023.findings-acl.830/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
15. [A Question Answering Benchmark Database for Hungarian](https://aclanthology.org/2023.law-1.19/) *— 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
16. [Adapting Pretrained Text-to-Text Models for Long Text Sequences](https://aclanthology.org/2023.findings-emnlp.370/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
17. [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://arxiv.org/abs/2310.03710) *— 2023 · arXiv:2310.03710*
18. [Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations](https://aclanthology.org/2023.acl-long.821/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
20. [AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking](https://aclanthology.org/2023.findings-acl.862/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
21. [Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture](https://aclanthology.org/2023.findings-emnlp.778/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
22. [Causal Reasoning of Entities and Events in Procedural Texts](https://aclanthology.org/2023.findings-eacl.31/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
23. [Closed-book Question Generation via Contrastive Learning](https://aclanthology.org/2023.eacl-main.230/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
24. [CoLT5: Faster Long-Range Transformers with Conditional Computation](https://aclanthology.org/2023.emnlp-main.309/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
25. [Comparing Neural Question Generation Architectures for Reading Comprehension](https://aclanthology.org/2023.bea-1.47/) *— 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)*
26. [DeepMaven: Deep Question Answering on Long-Distance Movie/TV Show Videos with Multimedia Knowledge Extraction and Synthesis](https://aclanthology.org/2023.eacl-main.221/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
27. [Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text](https://aclanthology.org/2023.ranlp-stud.1/) *— 2023 · Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing*
28. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
29. [Domain Incremental Lifelong Learning in an Open World](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
30. [Echoes from Alexandria: A Large Resource for Multilingual Book Summarization](https://aclanthology.org/2023.findings-acl.54/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
31. [Effective Long-Context Scaling of Foundation Models](https://arxiv.org/abs/2309.16039) *— 2023 · arXiv:2309.16039*
32. [Efficient Long-Text Understanding with Short-Text Models](https://aclanthology.org/2023.tacl-1.17/) *— 2023 · Transactions of the Association for Computational Linguistics*
33. [Efficient Streaming Language Models with Attention Sinks](https://arxiv.org/abs/2309.17453) *— 2023 · arXiv:2309.17453*
34. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
35. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Few-shot Unified Question Answering: Tuning Models or Prompts?](https://aclanthology.org/2023.findings-emnlp.550/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
37. [Functional Interpolation for Relative Positions Improves Long Context Transformers](https://arxiv.org/abs/2310.04418) *— 2023 · arXiv:2310.04418*
38. [GameQA: Gamified Mobile App Platform for Building Multiple-Domain Question-Answering Datasets](https://aclanthology.org/2023.eacl-demo.18/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations*
39. [Gemini: A Family of Highly Capable Multimodal Models](https://arxiv.org/abs/2312.11805) *— 2023 · arXiv:2312.11805*
40. [Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank](https://aclanthology.org/2023.bea-1.22/) *— 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)*
41. [In-Context Demonstration Selection with Cross Entropy Difference](https://aclanthology.org/2023.findings-emnlp.81/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
42. [InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining](https://arxiv.org/abs/2310.07713) *— 2023 · arXiv:2310.07713*
43. [L-Eval: Instituting Standardized Evaluation for Long Context Language Models](https://arxiv.org/abs/2307.11088) *— 2023 · arXiv:2307.11088*
44. [Learning to Filter Context for Retrieval-Augmented Generation](https://arxiv.org/abs/2311.08377) *— 2023 · arXiv:2311.08377*
45. [Learning to Generalize for Cross-domain QA](https://aclanthology.org/2023.findings-acl.84/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
46. [Long Story Generation Challenge](https://aclanthology.org/2023.inlg-genchal.2/) *— 2023 · Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges*
47. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://arxiv.org/abs/2308.14508) *— 2023 · arXiv:2308.14508*
49. [LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://arxiv.org/abs/2311.04939) *— 2023 · arXiv:2311.04939*
50. [MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.343/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
51. [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://aclanthology.org/2023.findings-acl.558/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
52. [Machine Reading Comprehension for Vietnamese Customer Reviews: Task, Corpus and Baseline Models](https://aclanthology.org/2023.paclic-1.3/) *— 2023 · Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation*
53. [Machine Reading Comprehension using Case-based Reasoning](https://aclanthology.org/2023.findings-emnlp.564/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
54. [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://nejlt.ep.liu.se/article/view/4725) *— 2023 · Northern European Journal of Language Technology*
55. [NarrativeXL: a Large-scale Dataset for Long-Term Memory Models](https://aclanthology.org/2023.findings-emnlp.1005/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
56. [PARROT: Zero-Shot Narrative Reading Comprehension via Parallel Reading](https://aclanthology.org/2023.findings-emnlp.895/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
57. [Personality Understanding of Fictional Characters during Book Reading](https://aclanthology.org/2023.acl-long.826/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Position: AI Evaluation Should Learn from How We Test Humans](https://arxiv.org/abs/2306.10512) *— 2023 · arXiv:2306.10512*
59. [Prompt Cache: Modular Attention Reuse for Low-Latency Inference](https://arxiv.org/abs/2311.04934) *— 2023 · MLSys 2024*
60. [Question Answer Generation in Bengali: Mitigating the scarcity of QA datasets in a low-resource language](https://aclanthology.org/2023.ijcnlp-main.29/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension](https://aclanthology.org/2023.findings-acl.306/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
62. [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://arxiv.org/abs/2310.01352) *— 2023 · arXiv:2310.01352*
63. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
64. [Rethinking Label Smoothing on Multi-hop Question Answering](https://aclanthology.org/2023.ccl-1.53/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
65. [Retrieval meets Long Context Large Language Models](https://arxiv.org/abs/2310.03025) *— 2023 · arXiv:2310.03025*
66. [Retrieval-Augmented Generation for Large Language Models: A Survey](https://arxiv.org/abs/2312.10997) *— 2023 · arXiv:2312.10997*
67. [RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems](https://aclanthology.org/2023.emnlp-demo.24/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
68. [Rogue Scores](https://aclanthology.org/2023.acl-long.107/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
69. [Sound of Story: Multi-modal Storytelling with Audio](https://aclanthology.org/2023.findings-emnlp.898/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
70. [Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks](https://aclanthology.org/2023.findings-acl.380/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
71. [Two-step Text Summarization for Long-form Biographical Narrative Genre](https://aclanthology.org/2023.codi-1.20/) *— 2023 · Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)*
72. [Unleashing the potential of prompt engineering for large language models](https://arxiv.org/abs/2310.14735) *— 2023 · arXiv:2310.14735 · Patterns 6(6) 101260 (2025)*
73. [Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://arxiv.org/abs/2305.01625) *— 2023 · arXiv:2305.01625*
74. [Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://aclanthology.org/2023.eacl-main.224/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
75. [ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding](https://aclanthology.org/2023.findings-emnlp.536/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
76. [A Relation Extraction Dataset for Knowledge Extraction from Web Tables](https://aclanthology.org/2022.coling-1.203/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
77. [ASQA: Factoid Questions Meet Long-Form Answers](https://aclanthology.org/2022.emnlp-main.566/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
78. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
79. [AugCSE: Contrastive Sentence Embedding with Diverse Augmentations](https://aclanthology.org/2022.aacl-main.30/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
80. [Augmenting eBooks with with recommended questions using contrastive fine-tuned T5](https://aclanthology.org/2022.icon-main.15/) *— 2022 · Proceedings of the 19th International Conference on Natural Language Processing (ICON)*
81. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
82. [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization](https://aclanthology.org/2022.acl-long.348/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension](https://md.research.elamur.ai/free/en/papers/xu-2022-fantastic-questions-where-find-them/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
84. [Holistic Evaluation of Language Models](https://arxiv.org/abs/2211.09110) *— 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023*
85. [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://arxiv.org/abs/2210.01241) *— 2022 · arXiv:2210.01241*
86. [It is AI”s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children”s Story Books](https://md.research.elamur.ai/free/en/papers/yao-2022-it-is-ai-s-turn/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [Learning and Evaluating Character Representations in Novels](https://aclanthology.org/2022.findings-acl.81/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
88. [Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition](https://aclanthology.org/2022.naacl-main.352/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
89. [Leveraging QA Datasets to Improve Generative Data Augmentation](https://aclanthology.org/2022.emnlp-main.660/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
90. [Lite Unified Modeling for Discriminative Reading Comprehension](https://aclanthology.org/2022.acl-long.594/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
91. [LittleBird: Efficient Faster & Longer Transformer for Question Answering](https://aclanthology.org/2022.emnlp-main.352/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
92. [MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://aclanthology.org/2022.findings-emnlp.500/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
93. [MixQG: Neural Question Generation with Mixed Answer Types](https://aclanthology.org/2022.findings-naacl.111/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
94. [MuLD: The Multitask Long Document Benchmark](https://aclanthology.org/2022.lrec-1.392/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
95. [Multi-VQG: Generating Engaging Questions for Multiple Images](https://aclanthology.org/2022.emnlp-main.19/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
96. [MultiSpanQA: A Dataset for Multi-Span Question Answering](https://aclanthology.org/2022.naacl-main.90/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
97. [Narrative Why-Question Answering: A Review of Challenges and Datasets](https://aclanthology.org/2022.gem-1.48/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
98. [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://aclanthology.org/2022.dialdoc-1.5/) *— 2022 · Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering*
99. [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://aclanthology.org/2022.naacl-main.313/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
100. [QuALITY: Question Answering with Long Input Texts, Yes!](https://aclanthology.org/2022.naacl-main.391/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*

Pages: [1](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/) · [2](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/2/) · **3** · [4](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/5/)